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Named Entity Recognition for Persian Literary Text: A Case Study on The Little Prince

Summary

Persian NER work has so far targeted news, social media and Wikipedia. Literary prose has been left aside, even though fiction often refers to entities through descriptions, figurative wording or story context rather than explicit proper names. According to the authors, their corpus is the earliest Persian dataset of literary text with hand-labeled named entities, and it is built from the Persian translation of The Little Prince taken from the PAMR corpus. The annotation scheme follows Borrega et al. (2007). It separates Strong Named Entities (proper names, rigid designators) from Weak Named Entities, which are discourse-unique noun phrases identified through Trigger Words. Three Persian NER tools trained on news and social media data are tested on this gold standard. Their output is also compared with ChatGPT annotations obtained by prompting it with the same guidelines. All tools perform considerably worse on the literary text, and their errors cluster around narrative-specific entities, metaphor and discourse structure. The publicly released corpus also supports ongoing Persian UMR development, where entity types inform argument interpretation, animacy and coreference.

Key points

  • The source text is the PAMR corpus (Takhshid et al., 2024), which had no prior NE labels. It contains 1,562 sentences, 14,427 tokens and 3,520 unique word forms, with sentence lengths of 1–65 tokens.
  • Preprocessing used Stanza for normalization and tokenization, and the data was formatted in the BIO scheme.
  • A noun phrase counts as a Weak Named Entity if it meets four criteria: a Trigger Word head, discourse uniqueness, trackability across mentions, and a distinct narrative role. An example is "the flower" as a character.
  • The evaluated systems are ParsNER, Shekar and ParsTwiNER, with guideline-instructed ChatGPT annotations as an additional comparison.
  • The work forms part of a broader effort to convert Persian AMR to UMR.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Topics: Named entity recognition · Discourse analysis

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/2jo2u37hdcot

@inproceedings{nassajian2026named,
  title = {{Named Entity Recognition for Persian Literary Text: A Case Study on The Little Prince}},
  author = {Nassajian, Minoo and Nivre, Joakim and Zeman, Daniel},
  booktitle = {Proceedings of The Seventh International Workshop on Designing Meaning Representations (DMR 2026) @ LREC 2026},
  year = {2026},
  doi = {10.63317/2jo2u37hdcot},
  url = {https://doi.org/10.63317/2jo2u37hdcot}
}

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